Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/147751
Title: PROBABILITY OF DEFAULT: MEASURING DEFAULT AND CREDIT RATING PREDICTIVE ABILITY
Authors: LOH YI CHENG
Issue Date: 2014
Citation: LOH YI CHENG (2014). PROBABILITY OF DEFAULT: MEASURING DEFAULT AND CREDIT RATING PREDICTIVE ABILITY. ScholarBank@NUS Repository.
Abstract: With the essential role of credit ratings clouded by conflicts of interest, alternative measures of default probability such as the NUS Risk Management Institute’s Probability of Default (RMI PD) have been gaining prominence. We select the RMI PD measure for our study to examine its ability to predict default and find that it exhibits a confirmative upward sloping trend that provides warning of a firm’s impending default. We also test its default prediction ability in-sample and arrive at a practical approach for users to employ the RMI PD for predicting default. We further tested its relation to credit ratings and find that it contributes to predicting for credit rating upgrade and downgrade decisions. We also applied the RMI PD to predict the magnitude of credit rating notch change and find that it is able to achieve up to 73% accuracy in its predicted ratings, with lower Type I and II errors for predicting credit rating changes of investment grade firms.
URI: http://scholarbank.nus.edu.sg/handle/10635/147751
Appears in Collections:Bachelor's Theses

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